1. Field
The techniques disclosed herein relate to image quality assessment, and in particular, to implementations of a deep neural network for qualification of images.
2. Description of the Related Art
There is an increasing demand for capabilities that provide for automatic assessment of image quality. Automatic assessment of image quality is not only useful for a variety of commercial and industrial applications, but also valuable to producers of imaging equipment. Typically, algorithms assessing quality of an image have required an ideal image, or “reference image” as a standard for comparison. Clearly, this can be problematic, as in many cases the reference images are not available.
The more useful and efficient alternatives are able to measure image quality without a reference image. This type of image assessment is referred to as non-reference image quality assessment (NRIQA). Generally, existing non-reference image quality assessment (NRIQA) methods suffer from two constraints: (1) requirement of hand-crafted features and (2) requirement of training data that is labeled with a quality score. Quite often, those require manual processing (i.e., human interaction). This can be time consuming and result in subjective interpretations. These two constraints make the non-reference image quality assessment (NRIQA) methods difficult to design and limit their applicability.
Thus, what are needed are improved techniques for more effectively and efficiently assessing quality of graphic images. The techniques should provide for automatic processing, and lend themselves to a variety of applications that require high quality assessments of image quality.